Abstract

Particle detection is a key procedure in particle field characterization with digital holography. Due to various background noises, spurious small particles might be generated and real small particles might be lost during particle detection. Therefore, accurate small particle detection remains a challenge in the research of energy and combustion. A deep learning method based on modified fully convolutional networks is proposed to detect small opaque particles (e.g., coal particles) on extended focus images. The model is tested by several experiments and proved to have good small particle detection accuracy.

© 2019 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Automated red blood cells extraction from holographic images using fully convolutional neural networks

Faliu Yi, Inkyu Moon, and Bahram Javidi
Biomed. Opt. Express 8(10) 4466-4479 (2017)

Fast particle characterization using digital holography and neural networks

B. Schneider, J. Dambre, and P. Bienstman
Appl. Opt. 55(1) 133-139 (2016)

Focus prediction in digital holographic microscopy using deep convolutional neural networks

Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton
Appl. Opt. 58(5) A202-A208 (2019)

References

  • View by:
  • |
  • |
  • |

  1. Y. S. Choi and S. J. Lee, “Three-dimensional volumetric measurement of red blood cell motion using digital holographic microscopy,” Appl. Opt. 48, 2983–2990 (2009).
    [Crossref]
  2. J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42, 531–555 (2010).
    [Crossref]
  3. X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
    [Crossref]
  4. X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
    [Crossref]
  5. X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
    [Crossref]
  6. Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
    [Crossref]
  7. L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
    [Crossref]
  8. S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
    [Crossref]
  9. L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
    [Crossref]
  10. Y. Yang and B. S. Kang, “Digital particle holographic system for measurements of spray field characteristics,” Opt. Laser Eng. 49, 1254–1263 (2011).
    [Crossref]
  11. J. Gao, D. R. Guildenbecher, P. L. Reu, and J. Chen, “Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method,” Opt. Express 21, 26432–26449 (2013).
    [Crossref]
  12. D. R. Guildenbecher, J. Gao, P. L. Reu, and J. Chen, “Digital holography simulations and experiments to quantify the accuracy of 3D particle location and 2D sizing using a proposed hybrid method,” Appl. Opt. 52, 3790–3801 (2013).
    [Crossref]
  13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems (2012).
  14. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556 (2014).
  15. C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.
  16. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.
  17. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.
  18. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
    [Crossref]
  19. Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
    [Crossref]
  20. Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
    [Crossref]
  21. T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202–A208 (2019).
    [Crossref]
  22. H. Wang, M. Lyu, and G. Situ, “eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,” Opt. Express 26, 22603–22614 (2018).
    [Crossref]
  23. T. Shimobaba, T. Takahashi, Y. Yamamoto, Y. Endo, and T. Ito, “Digital holographic particle volume reconstruction using a deep neural network,” Appl. Opt. 58, 1900–1906 (2018).
    [Crossref]
  24. Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27, 13581–13595 (2019).
    [Crossref]
  25. F. L. Yi, I. Moon, and B. Javidi, “Automated red blood cells extraction from holographic images using fully convolutional neural networks,” Biomed. Opt. Express 8, 4466–4479 (2017).
    [Crossref]
  26. Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
    [Crossref]
  27. T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043–15057 (2017).
    [Crossref]
  28. K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).
    [Crossref]
  29. G. Zhang, T. Guan, Z. Shen, X. Wang, T. Hu, D. Wang, Y. He, and N. Xie, “Fast phase retrieval in off-axis digital holographic microscopy through deep learning,” Opt. Express 26, 19388–19405 (2018).
    [Crossref]
  30. E. Y. Lam, Z. Ren, and Z. Xu, “Learning-based nonparametric autofocusing for digital holography,” Optica 5, 337–344 (2018).
    [Crossref]
  31. A. Sinha, J. Lee, S. Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4, 1117–1125 (2017).
    [Crossref]
  32. T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.
  33. Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.
  34. D.-Y. Park and J.-H. Park, “Generation of high-resolution and speckle reduced light field data from hologram using deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.33.
  35. S.-C. Liu, J. Li, and D. Chu, “Calculating real-time computer-generated holograms for holographic 3D displays through deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Tu4A.7.
  36. X. Miao, X. Yuan, and P. Wilford, “Deep learning for compressive spectral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.3.
  37. S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
    [Crossref]
  38. M. D. Hannel, A. Abdulali, M. O’Brien, and D. G. Grier, “Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles,” Opt. Express 26, 15221–15231 (2018).
    [Crossref]
  39. T. Pitkäaho, A. Manninen, and T. J. Naughton, “Digital hologram reconstruction segmentation using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.1.
  40. T. Pitkäaho, A. Manninen, and T. J. Naughton, “Detection of an object in the field of view of a digital hologram with an heuristic algorithm parameterized using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.3.
  41. Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
    [Crossref]
  42. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).
  43. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
  44. A. Vedaldi and K. Lenc, “MatConvNet -- convolutional neural networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (2015).
  45. S. Ehrhardt and A. Vedaldi, “A MatConvNet-based implementation of the fully-convolutional networks for image segmentation,” Andrea Vedaldi, 2016, https://github.com/vlfeat/matconvnet-fcn .
  46. A. Vedaldi and K. Lenc, “Pretrained models: ImageNet ILSVRC classification,” 2015, http://www.vlfeat.org/matconvnet/pretrained/#imagenet-ilsvrc-classification .
  47. K. Matsushima and T. Shimobaba, “Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields,” Opt. Express 17, 19662–19673 (2009).
    [Crossref]
  48. X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
    [Crossref]
  49. Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
    [Crossref]
  50. L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

2019 (6)

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202–A208 (2019).
[Crossref]

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27, 13581–13595 (2019).
[Crossref]

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).
[Crossref]

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

2018 (9)

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

M. D. Hannel, A. Abdulali, M. O’Brien, and D. G. Grier, “Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles,” Opt. Express 26, 15221–15231 (2018).
[Crossref]

G. Zhang, T. Guan, Z. Shen, X. Wang, T. Hu, D. Wang, Y. He, and N. Xie, “Fast phase retrieval in off-axis digital holographic microscopy through deep learning,” Opt. Express 26, 19388–19405 (2018).
[Crossref]

E. Y. Lam, Z. Ren, and Z. Xu, “Learning-based nonparametric autofocusing for digital holography,” Optica 5, 337–344 (2018).
[Crossref]

H. Wang, M. Lyu, and G. Situ, “eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,” Opt. Express 26, 22603–22614 (2018).
[Crossref]

T. Shimobaba, T. Takahashi, Y. Yamamoto, Y. Endo, and T. Ito, “Digital holographic particle volume reconstruction using a deep neural network,” Appl. Opt. 58, 1900–1906 (2018).
[Crossref]

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

2017 (5)

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

A. Sinha, J. Lee, S. Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4, 1117–1125 (2017).
[Crossref]

F. L. Yi, I. Moon, and B. Javidi, “Automated red blood cells extraction from holographic images using fully convolutional neural networks,” Biomed. Opt. Express 8, 4466–4479 (2017).
[Crossref]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043–15057 (2017).
[Crossref]

2015 (3)

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

2014 (2)

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

2013 (2)

2012 (1)

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

2011 (1)

Y. Yang and B. S. Kang, “Digital particle holographic system for measurements of spray field characteristics,” Opt. Laser Eng. 49, 1254–1263 (2011).
[Crossref]

2010 (1)

J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42, 531–555 (2010).
[Crossref]

2009 (2)

2006 (1)

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

2005 (1)

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Abdulali, A.

Adwani, P.

T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.

Allano, D.

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Alliez, P.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).

Anguelov, D.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Barbastathis, G.

Braeken, D.

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Brunel, M.

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Bui, V.

Castro, C. M.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Cen, K.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Cen, K. F.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Chang, L. C.

Charpiat, G.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).

Chen, J.

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

J. Gao, D. R. Guildenbecher, P. L. Reu, and J. Chen, “Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method,” Opt. Express 21, 26432–26449 (2013).
[Crossref]

D. R. Guildenbecher, J. Gao, P. L. Reu, and J. Chen, “Digital holography simulations and experiments to quantify the accuracy of 3D particle location and 2D sizing using a proposed hybrid method,” Appl. Opt. 52, 3790–3801 (2013).
[Crossref]

Chen, L.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Chen, L. H.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

Choi, H. J.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Choi, M. C.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Choi, N. R.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Choi, Y. S.

Chu, D.

S.-C. Liu, J. Li, and D. Chu, “Calculating real-time computer-generated holograms for holographic 3D displays through deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Tu4A.7.

Coetmellec, S.

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Di, J.

Endo, Y.

Erhan, D.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Gao, J.

Gao, X.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

Gnaydin, H.

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Grehan, G.

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Grier, D. G.

Guan, T.

Guildenbecher, D. R.

Gunaydin, H.

Hannel, M. D.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.

He, Y.

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems (2012).

Hu, T.

Im, H.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Ito, T.

Javidi, B.

Ji, X.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

Jia, Y. Q.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Jo, Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Joo, H.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Jung, J.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Kang, B. S.

Y. Yang and B. S. Kang, “Digital particle holographic system for measurements of spray field characteristics,” Opt. Laser Eng. 49, 1254–1263 (2011).
[Crossref]

Kang, S. J.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Katz, J.

J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42, 531–555 (2010).
[Crossref]

Kemao, Q.

Kim, M. H.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Kim, S.-J.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Kompenhans, J.

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems (2012).

Lagae, L.

Lam, E. Y.

Lam, V.

Lambrechts, A.

Lebrun, D.

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Lee, H.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Lee, J.

Lee, K.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Lee, S. J.

Lee, S. Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Lenc, K.

A. Vedaldi and K. Lenc, “MatConvNet -- convolutional neural networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (2015).

Li, J.

S.-C. Liu, J. Li, and D. Chu, “Calculating real-time computer-generated holograms for holographic 3D displays through deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Tu4A.7.

Li, S.

Li, Y.

Lin, X.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

Lin, X. D.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

Liu, S.-C.

S.-C. Liu, J. Li, and D. Chu, “Calculating real-time computer-generated holograms for holographic 3D displays through deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Tu4A.7.

Liu, W.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Luo, Z.

Lyu, M.

Maggiori, E.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).

Malek, M.

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Manninen, A.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202–A208 (2019).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Digital hologram reconstruction segmentation using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.1.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Detection of an object in the field of view of a digital hologram with an heuristic algorithm parameterized using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.3.

Matsushima, K.

Meunier-Guttin-Cluzel, S.

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Miao, X.

X. Miao, X. Yuan, and P. Wilford, “Deep learning for compressive spectral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.3.

Min, J.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Moon, I.

Naughton, T. J.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202–A208 (2019).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Detection of an object in the field of view of a digital hologram with an heuristic algorithm parameterized using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.3.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Digital hologram reconstruction segmentation using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.1.

Nehmetallah, G.

T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043–15057 (2017).
[Crossref]

T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.

Nguyen, T.

T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043–15057 (2017).
[Crossref]

T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.

O’Brien, M.

Ozcan, A.

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Park, D.-Y.

D.-Y. Park and J.-H. Park, “Generation of high-resolution and speckle reduced light field data from hologram using deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.33.

Park, J.-H.

D.-Y. Park and J.-H. Park, “Generation of high-resolution and speckle reduced light field data from hologram using deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.33.

Park, S.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Park, Y.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Patte-Rouland, B.

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Piao, Y.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

Pitkäaho, T.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202–A208 (2019).
[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Digital hologram reconstruction segmentation using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.1.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Detection of an object in the field of view of a digital hologram with an heuristic algorithm parameterized using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.3.

Pu, S. L.

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Qiu, K. Z.

Rabinovich, A.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Raub, C. B.

Reed, S.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.

Ren, Z.

Reu, P. L.

Reumers, V.

Rivenson, Y.

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

Rong, Z.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Saengkaew, S.

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Schroder, A.

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

Sermanet, P.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Shen, G. X.

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

Shen, Z.

Sheng, J.

J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42, 531–555 (2010).
[Crossref]

Shimobaba, T.

Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556 (2014).

Sinha, A.

Situ, G.

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Stahl, R.

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems (2012).

Szegedy, C.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Tadros, J.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Takahashi, T.

Tarabalka, Y.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).

Teng, D.

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Thai, A.

T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.

Vanhoucke, V.

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

Vedaldi, A.

A. Vedaldi and K. Lenc, “MatConvNet -- convolutional neural networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (2015).

Wang, C.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Wang, D.

Wang, H.

Wang, K.

Wang, X.

Wang, Z. H.

Wei, Z. S.

Weissleder, R.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Wilford, P.

X. Miao, X. Yuan, and P. Wilford, “Deep learning for compressive spectral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.3.

Wu, C.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Wu, C. Y.

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

Wu, X.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Wu, X. C.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

Wu, Y.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Wu, Y. C.

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Y. C. Wu, X. C. Wu, J. Yang, Z. H. Wang, X. Gao, B. W. Zhou, L. H. Chen, K. Z. Qiu, G. Grehan, and K. F. Cen, “Wavelet-based depth-of-field extension, accurate autofocusing, and particle pairing for digital inline particle holography,” Appl. Opt. 53, 556–564 (2014).
[Crossref]

Xie, N.

Xu, Z.

Xue, Z. L.

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

Yamamoto, Y.

Yang, J.

Yang, Y.

Y. Yang and B. S. Kang, “Digital particle holographic system for measurements of spray field characteristics,” Opt. Laser Eng. 49, 1254–1263 (2011).
[Crossref]

Yao, L.

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

Yao, L. C.

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

L. C. Yao, X. C. Wu, Y. C. Wu, J. Yang, X. Gao, L. H. Chen, G. Grehan, and K. F. Cen, “Characterization of atomization and breakup of acoustically levitated drops with digital holography,” Appl. Opt. 54, A23–A31 (2015).
[Crossref]

Yi, F. L.

Yoon, J.

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Yuan, X.

X. Miao, X. Yuan, and P. Wilford, “Deep learning for compressive spectral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.3.

Yurt, A.

Zhang, G.

Zhang, M.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.

Zhang, Y.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

Zhang, Y. B.

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Y. C. Wu, Y. Rivenson, Y. B. Zhang, Z. S. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5, 704–710 (2018).
[Crossref]

Zhang, Y. G.

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

Zhao, B.

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Zhao, H. F.

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

Zhao, J.

Zheng, C. H.

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

Zhou, B. W.

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556 (2014).

Annu. Rev. Fluid Mech. (1)

J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42, 531–555 (2010).
[Crossref]

Appl. Opt. (6)

Biomed. Opt. Express (1)

Energy Fuel (1)

X. C. Wu, L. C. Yao, Y. C. Wu, X. D. Lin, L. H. Chen, J. Chen, X. Gao, and K. F. Cen, “In-situ characterization of coal particle combustion via long working distance digital in-line holography,” Energy Fuel 32, 8277–8286 (2018).
[Crossref]

Exp. Fluids (1)

S. L. Pu, D. Allano, B. Patte-Rouland, M. Malek, D. Lebrun, and K. F. Cen, “Particle field characterization by digital in-line holography: 3D location and sizing,” Exp. Fluids 39, 1–9 (2005).
[Crossref]

Fuel (3)

X. Lin, Y. Wu, C. Wu, L. Yao, X. Wu, L. Chen, and K. Cen, “Evolution of volatile cloud in pulverized coal combustion with high-speed digital inline holographic visualization,” Fuel 241, 199–206 (2019).
[Crossref]

Y. Wu, L. Yao, X. Wu, J. Chen, G. Grehan, and K. Cen, “3D imaging of individual burning char and volatile plume in a pulverized coal flame with digital inline holography,” Fuel 206, 429–436 (2017).
[Crossref]

X. C. Wu, Z. L. Xue, H. F. Zhao, L. C. Yao, L. H. Chen, C. H. Zheng, and X. Gao, “Measurement of slurry droplets by digital holographic microscopy: fundamental research,” Fuel 158, 697–704 (2015).
[Crossref]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Laser Optoelectron. Prog. (1)

L. C. Yao, X. C. Wu, X. D. Lin, Y. C. Wu, L. H. Chen, X. Gao, and K. F. Cen, “Measurement of burning biomass particles via high-speed digital holography,” Laser Optoelectron. Prog. 56, 68–74 (2019).

Lect. Notes Comput. Sci. (1)

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Lect. Notes Comput. Sci. 9351, 234–241 (2015).
[Crossref]

Light Sci. Appl. (1)

Y. Rivenson, Y. B. Zhang, H. Gnaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

Opt. Commun. (1)

X. Wu, S. Meunier-Guttin-Cluzel, Y. Wu, S. Saengkaew, D. Lebrun, M. Brunel, L. Chen, S. Coetmellec, K. Cen, and G. Grehan, “Holography and micro-holography of particle fields: a numerical standard,” Opt. Commun. 285, 3013–3020 (2012).
[Crossref]

Opt. Eng. (1)

Y. G. Zhang, G. X. Shen, A. Schroder, and J. Kompenhans, “Influence of some recording parameters on digital holographic particle image velocimetry,” Opt. Eng. 45, 075801 (2006).
[Crossref]

Opt. Express (8)

K. Matsushima and T. Shimobaba, “Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields,” Opt. Express 17, 19662–19673 (2009).
[Crossref]

T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043–15057 (2017).
[Crossref]

J. Gao, D. R. Guildenbecher, P. L. Reu, and J. Chen, “Uncertainty characterization of particle depth measurement using digital in-line holography and the hybrid method,” Opt. Express 21, 26432–26449 (2013).
[Crossref]

M. D. Hannel, A. Abdulali, M. O’Brien, and D. G. Grier, “Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles,” Opt. Express 26, 15221–15231 (2018).
[Crossref]

G. Zhang, T. Guan, Z. Shen, X. Wang, T. Hu, D. Wang, Y. He, and N. Xie, “Fast phase retrieval in off-axis digital holographic microscopy through deep learning,” Opt. Express 26, 19388–19405 (2018).
[Crossref]

H. Wang, M. Lyu, and G. Situ, “eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,” Opt. Express 26, 22603–22614 (2018).
[Crossref]

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27, 13581–13595 (2019).
[Crossref]

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).
[Crossref]

Opt. Laser Eng. (1)

Y. Yang and B. S. Kang, “Digital particle holographic system for measurements of spray field characteristics,” Opt. Laser Eng. 49, 1254–1263 (2011).
[Crossref]

Optica (3)

Proc. Combust. Inst. (1)

L. C. Yao, C. Y. Wu, Y. C. Wu, L. H. Chen, J. Chen, X. C. Wu, and K. F. Cen, “Investigating particle and volatile evolution during pulverized coal combustion using high-speed digital in-line holography,” Proc. Combust. Inst. 37, 2911–2918 (2019).
[Crossref]

Sci. Adv. (1)

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3, e1700606 (2017).
[Crossref]

Sci. Rep. (1)

S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee, “Deep transfer learning-based hologram classification for molecular diagnostics,” Sci. Rep. 8, 17003 (2018).
[Crossref]

Other (16)

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” in Geoscience & Remote Sensing Symposium (2016).

A. Vedaldi and K. Lenc, “MatConvNet -- convolutional neural networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (2015).

S. Ehrhardt and A. Vedaldi, “A MatConvNet-based implementation of the fully-convolutional networks for image segmentation,” Andrea Vedaldi, 2016, https://github.com/vlfeat/matconvnet-fcn .

A. Vedaldi and K. Lenc, “Pretrained models: ImageNet ILSVRC classification,” 2015, http://www.vlfeat.org/matconvnet/pretrained/#imagenet-ilsvrc-classification .

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Digital hologram reconstruction segmentation using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.1.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Detection of an object in the field of view of a digital hologram with an heuristic algorithm parameterized using a convolutional neural network,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Th3A.3.

T. Nguyen, A. Thai, P. Adwani, and G. Nehmetallah, “Autofocusing of fluorescent microscopic images through deep learning convolutional neural networks,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.32.

Y. Piao, Z. Rong, M. Zhang, Y. Zhang, and X. Ji, “Deep learning for single view focal plane reconstruction in integral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.2.

D.-Y. Park and J.-H. Park, “Generation of high-resolution and speckle reduced light field data from hologram using deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper W3A.33.

S.-C. Liu, J. Li, and D. Chu, “Calculating real-time computer-generated holograms for holographic 3D displays through deep learning,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper Tu4A.7.

X. Miao, X. Yuan, and P. Wilford, “Deep learning for compressive spectral imaging,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2019), paper M3B.3.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems (2012).

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556 (2014).

C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 770–778.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (15)

Fig. 1.
Fig. 1. Schematic of the high-speed DIH system for measuring burning coal particles.
Fig. 2.
Fig. 2. Figure of dataset (a) input grayscale image (size: ${320} \times {320}$ pixel). (b) Ground truth label (size: ${320} \times {320}$ pixel). (c) Prediction of the model.
Fig. 3.
Fig. 3. (a) Architecture of FCN-8S; (b) architecture of the deep-learning model based on FCN-8S.
Fig. 4.
Fig. 4. Accuracy and loss curves of model training.
Fig. 5.
Fig. 5. Intensity distribution of the simulated particles (diameter: 25 µm) with distance to the particle center respectively calculated by ASM and LMT.
Fig. 6.
Fig. 6. Detailed results of Sim1.
Fig. 7.
Fig. 7. Detailed results of Sim2.
Fig. 8.
Fig. 8. Representative results of Sim2. (a) EFIs (full size: ${1024} \times {1024}$ pixels); (b) result of modified Fcn-8s model (full size: ${1024} \times {1024}$ pixels).
Fig. 9.
Fig. 9. (a) Experimental setup of DIH system for measuring calibration board. (b) One extended focus image (size: ${2048} \times {2048}$ pixels) when recording distance is 116 mm. (c) Cropped image (size: ${640} \times {640}$ pixels) from (b) containing all dots of 10 and 50 µm. (d) Cropped image (size: ${72} \times {72}$ pixels) from (c) containing 25 dots of 10 µm.
Fig. 10.
Fig. 10. (a) Detection accuracy of tiny dots at different recording distances. (b) Mean small particle (10 µm) detection accuracy.
Fig. 11.
Fig. 11. (a) Cropped EFIs (size: ${72} \times {72}$ pixels). (b) Results of modified Fcn-8s model. (c) Results of threshold-based method with ${128} \times {128}$ local block.
Fig. 12.
Fig. 12. (a) Cropped EFIs of burning bamboo powders (size: ${320} \times {320}$ pixels); (b) Corresponding results of modified Fcn-8s model (size: ${320} \times {320}$ pixels); (c) Cropped EFIs of burning coal particles (size: ${320} \times {320}$ pixels); (d) Corresponding results of modified Fcn-8s model (size: ${320} \times {320}$ pixels).
Fig. 13.
Fig. 13. Fuel: bamboo powders. (a) Extended focus image (full size: ${1280} \times {960}$ pixels). (b) Result of modified Fcn-8s model (full size: ${1280} \times {960}$ pixels). (c) Images inside the red dotted line boxes (size: ${320} \times {320}$ pixels). (d) Corresponding binarized images cropped from (b). (e)–(h) Corresponding binarized images cropped from threshold-based method results with ${128} \times {128}$ , ${256} \times {256}$ , ${512} \times {512}$ , and ${1280} \times {960}$ local blocks, respectively.
Fig. 14.
Fig. 14. Fuel: lignite particles. (a) Extended focus image (full size: ${1280} \times {960}$ pixels). (b) Result of modified Fcn-8s model (full size: ${1280} \times {960}$ pixels). (c) Images inside the red dotted line boxes (size: ${320} \times {320}$ pixels). (d) Corresponding binarized images cropped from (b). (e)–(h) Corresponding binarized images cropped from threshold-based method results with ${128} \times {128}$ , ${256} \times {256}$ , ${512} \times {512}$ , and ${1280} \times {960}$ local blocks, respectively.
Fig. 15.
Fig. 15. Fuel: bamboo powders. (a) Extended focus image (full size: ${1280} \times {960}$ pixels). (b) Result of modified Fcn-8s model (full size: ${1280} \times {960}$ pixels). (c) Images inside the red dotted line boxes (size: ${320} \times {320}$ pixels). (d) Corresponding binarized images cropped from (b). (e)–(h) Corresponding binarized images cropped from threshold-based method results with ${128} \times {128}$ , ${256} \times {256}$ , ${512} \times {512}$ , and ${1280} \times {960}$ local blocks, respectively.

Tables (2)

Tables Icon

Table 1. Evaluation of Model

Tables Icon

Table 2. Mean Detection Accuracy of Real Particles of Sim2

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

P A = i P i i i T i ,
M P A = 1 k c i P i i T i ,
M I O U = 1 k c i P i i ( T i + j P j i P i i ) ,
A c = i = 1 i m c p i m × p a r ,

Metrics